Generating credit building recommendations through machine learning analysis of user activity-based feedback

A system comprising:

• a communications interface capable of receiving data from a client device;
• a memory configured to store information identifying a plurality of user accounts and, in association with each of the plurality of user accounts, (i) activity data relating to historical activity on the account and (ii) financial data associated with an account holder, the financial data comprising a credit score of the account holder;
• and
• at least one processor configured to:
• train one or more machine learning models based on a training set of data derived from the activity data and financial data stored in the memory;
• receive goal data from the client device via the communications interface, the goal data corresponding to at least one user account of the plurality of user accounts and comprising a user-defined goal and a desired timeline for achieving the user-defined goal, wherein a user of the client device is an account holder of the at least one user account;
• obtain, from the memory, activity data associated with the at least one user account;
• obtain financial data associated with the user of the client device from a third party server located remotely from the system, the obtained financial data comprising a credit score of the user of the client device;
• receive, in real time, activity data indicative of a transaction being attempted by the user;
• apply the one or more machine learning models to a data set comprising the obtained activity data, the obtained financial data, the received goal data, and the activity data indicative of the transaction to predict an impact of the transaction to the user-defined goal;
• generate a recommendation regarding the transaction based on the predicted impact;
• transmit the generated recommendation regarding the transaction to the client device via the communications interface prior to completion of the transaction;
• apply the one or more machine learning models to a dataset comprising the obtained activity data, the obtained financial data, and the received goal data, to identify a set of potential user activities for improving the credit score of the user based on the received goal data;
• generate one or more recommendations of user activity for improving the credit score of the user based on the identified set of potential user activities;
• identify a plurality of applications installed on the client device;
• determine an application of the plurality of applications that corresponds to income generation;
• and
• include, in the one or more recommendations of user activity, a recommendation to use the application to achieve further income.